code: def select_action(self, s_t, episode):
      # assert episode >= self.warmup, 'Episode: {} warmup: {}'.format(episode, self.warmup)
      action = to_numpy(
          self.actor(to_tensor(np.array(s_t).reshape(1, -1)))
      ).squeeze(0)
      delta = self.init_delta * (
          self.delta_decay ** (episode - self.warmup)
      )
      # action += self.is_training * max(self.epsilon, 0) * self.random_process.sample()
      action = self.sample_from_truncated_normal_distribution(
          lower=self.lbound, upper=self.rbound, mu=action, sigma=delta
      )
      action = np.clip(action, self.lbound, self.rbound)
  
      # self.a_t = action
      return action
  
imports: import numpy
  import torch
  import torch.nn
  from torch.optim import Adam
  from .memory import SequentialMemory
  from .utils import to_numpy, to_tensor
label_desc: Use image node_id 7 for calling the DDPG obj's underlying member method code with example usage: obj.select_action(s_t, episode) and returns: action